7 Leading Neocloud Providers and What Their Kubernetes Stack Is Missing

Summary

• Neoclouds offer high-density GPU clusters for AI workloads at a fraction of the cost of hyperscalers—up to 66% cheaper for an NVIDIA H100 instance.
• The main challenge with most neoclouds isn't the hardware but the incomplete orchestration layer, which leads to slow tenant onboarding, weak security isolation, and significant DIY management overhead.
• The vCluster Platform closes this orchestration gap by enabling fast, secure, and fully isolated Kubernetes clusters for tenants, turning raw GPU access into a true managed AI cloud experience.

The AI revolution doesn't run on hype — it runs on GPUs. And that demand has birthed a new class of infrastructure provider: the neocloud. Unlike traditional hyperscalers like AWS or Azure, neoclouds are purpose-built for AI workloads, offering access to high-density GPU clusters at a fraction of the cost. According to Equinix, an average NVIDIA DGX H100 instance costs $34/hour on neoclouds versus $98/hour on hyperscalers — a 66% reduction that's impossible to ignore. With the GPU as a Service (GPUaaS) market projected to grow from $3.80B in 2024 to $12.26B by 2030, the race for AI infrastructure dominance is well underway.

But here's what the benchmarks don't show you: raw GPU access is just table stakes.

The teams actually building AI products — training LLMs, running inference at scale, developing AI platforms with isolated tenant environments — aren't just shopping for H100s. They're shopping for the entire operational experience around those H100s. And that's where most neoclouds quietly fall short.

Talk to any AI/ML team that's been through a neocloud deployment and you'll hear the same frustrations: slow tenant onboarding that blocks time-to-value, namespace-only isolation that makes security teams nervous, and enough DIY Kubernetes overhead to keep a platform engineering team busy indefinitely. The hardware is there. The orchestration often isn't.

This article profiles seven of the most prominent neocloud providers — CoreWeave, Nscale, Lambda, Crusoe, Nebius, Civo, and Vultr — using a consistent scorecard: GPU fleet size, regions, bare metal provisioning approach, and tenant isolation model. More importantly, we'll be honest about where each provider's Kubernetes stack creates real friction for end customers.

1. CoreWeave

CoreWeave is widely regarded as the neocloud benchmark — the only provider rated "Platinum" in community comparisons. Their CoreWeave Kubernetes Service (CKS) is purpose-built for high-performance workloads, with each cluster running inside its own Virtual Private Cloud for strong network-level separation.

Where the friction lives: The power comes with operational complexity. Customers managing their own cluster configurations — especially around GPU drivers — can easily run into conflicts. CoreWeave explicitly warns against manually installing the NVIDIA GPU Operator, which can clash with platform-managed components. Spinning up new isolated tenant environments also requires more manual intervention than a fully virtualized control plane solution would demand. For teams that need to onboard dozens of tenants quickly, that overhead compounds fast.

2. Nscale

Nscale is a strong European neocloud contender, offering competitive GPU capacity and solid regional infrastructure. It's a popular choice for enterprises wanting proximity to EU data centers.

Where the friction lives: Namespace-only isolation creates a shared blast radius — if one tenant's workload goes sideways, neighboring workloads on the same node can feel it. Beyond that, customers frequently report significant DIY control-plane overhead, with the burden of managing Kubernetes configurations falling squarely on the customer. Provisioning isolated environments for each new tenant is a manual, slow process that delays both time-to-value and revenue.

3. Lambda

Lambda has carved out a solid reputation in the AI community, particularly among researchers who value its straightforward pricing and GPU availability. When you can get capacity, it performs well.

Where the friction lives: Availability is the most cited frustration — waitlists are a recurring reality. Beyond that, the Kubernetes environment demands heavy manual configuration. Teams with deep Kubernetes expertise can navigate it, but anyone expecting a managed, self-service experience will find themselves doing far more platform engineering than anticipated. Namespace-level isolation also limits what you can safely offer for workloads requiring tenant isolation.

4. Crusoe

Crusoe is the sustainability play in the neocloud market — they run GPU workloads on energy derived from otherwise-flared natural gas, giving sustainability-conscious enterprises a compelling angle. It's a genuine differentiator.

Where the friction lives: The sustainability story is strong; the orchestration story is not. Provisioning new tenant clusters on bare metal is slow and involves manual steps that don't scale well as customer counts grow. Namespace-only isolation raises familiar concerns about workload separation — especially for customers running untrusted or sensitive jobs. Managing resource policies across namespaces for different teams quickly becomes error-prone without stronger isolation primitives.

5. Nebius

Nebius is the aggressive price leader. Their custom ODM hardware design compresses gross margins from a typical 10–15% down to around 2%, enabling the lowest absolute GPU pricing in the market. If you're pure cost-optimizing, Nebius is hard to beat on paper.

Where the friction lives: The user research here is unambiguous. Community discussions describe the UI/UX as "overly complex and unintuitive, creating friction that deters less technically inclined customers." Beyond the interface, Nebius lags on operational visibility — competitors offer out-of-the-box Grafana dashboards and active health checks, while Nebius requires customers to build this observability layer themselves. Low price doesn't help much if your team is spending cycles wiring up monitoring and fighting a confusing control panel. Nebius has acknowledged these UI/UX shortcomings and is working on improvements, but the gap remains real today.

6. Civo

Civo has built a loyal following among developers who want fast, affordable Kubernetes without the enterprise complexity. Their developer experience is genuinely good for single-tenant, low-stakes workloads.

Where the friction lives: The simplicity that makes Civo appealing for small teams becomes a liability at scale. Running AI workloads with tenant isolation on Civo typically requires building substantial custom tooling and automation — the platform doesn't come with the primitives to handle it natively. Namespace isolation isn't strong enough for serious tenant isolation, and the infrastructure agility to respond to rapid GPU demand fluctuations is limited. Teams that outgrow the simple use case often hit a ceiling fast.

7. Vultr

Vultr's strength is its global reach and reliable bare metal deployment speed. For teams that need geographic distribution and predictable infrastructure, it's a solid baseline.

Where the friction lives: The managed Kubernetes layer is where Vultr's "managed" label gets tested. In practice, tenants take on extensive management responsibility for their own Kubernetes environments — configuration, upgrades, monitoring, and isolation all require customer effort. The standard namespace model means noisy-neighbor problems are a real risk in shared environments, and the lack of a deeper orchestration layer means what's marketed as managed can feel closer to self-managed in day-to-day operations.

The Pattern Is Clear — And It Points to One Gap

Across all seven providers, a consistent picture emerges: strong GPU hardware, incomplete orchestration. Every provider on this list can get you GPUs. What separates a commodity GPU rental from a true managed AI cloud is what happens above the hardware layer — the orchestration stack that handles tenant onboarding, isolation, lifecycle management, and operational visibility.

The friction points map to three recurring failure modes:

• Slow onboarding — manually provisioning isolated environments for each new tenant doesn't scale
• Weak isolation — namespace-level separation creates shared blast radius and security exposure
• DIY control-plane overhead — customers absorbing operational burden that the platform should own

This is exactly the gap that vCluster was built to close — and it's already doing so in production at neoclouds like CoreWeave and Nscale.

Closing the Orchestration Gap with vCluster

vCluster takes a fundamentally different approach to Kubernetes for AI infrastructure. Instead of provisioning full physical clusters per tenant (expensive and slow) or splitting tenants into shared namespaces (weak isolation), vCluster virtualizes the Kubernetes control plane itself — running CNCF-certified K8s clusters as lightweight processes inside a host cluster.

Each tenant gets their own API server, etcd, RBAC, and CRDs in seconds, not days. It's a genuine cluster-admin experience without the cost and latency of spinning up physical clusters.

Here's how that maps directly onto the friction points identified above:

Slow onboarding → Instant, self-service tenant clusters. The vCluster Platform creates fully isolated tenant environments in seconds. For neocloud providers, this means you can offer an EKS/GKE-like self-service experience to your customers without any new platform engineering hires. Boost Run launched with vCluster in under 45 days with zero new hires.

Namespace-only isolation → Full isolation spectrum. vCluster provides a configurable isolation spectrum — from shared nodes to dedicated VMs — combined with vNode, a kernel-native workload isolation layer that delivers container breakout protection without hypervisor overhead. No VM tax. No shared blast radius. Bare metal GPU performance preserved.

DIY control-plane overhead → Zero-touch bare metal to Kubernetes. vMetal handles the entire path from rack to production: PXE boot, OS installation, machine registration, and network automation — all zero-touch. vCluster Standalone runs as a single binary directly on bare metal Linux, with no dependency on k3s, kubeadm, or any intermediate K8s layer. Lintasarta used this stack to launch Indonesia's leading GPU cloud in 90 days with 170+ tenant clusters.

The scale is production-proven: 29.8k GitHub stars, 100K+ GPU nodes powered, 40M+ tenant clusters created, and inclusion in the NVIDIA DGX SuperPOD reference architecture. Customers include CoreWeave, Nscale, JPMorganChase, and Adobe.

The Takeaway

The neocloud market is real, growing fast, and essential for democratizing access to AI compute. But as the market matures, the differentiator is shifting. The neoclouds that win long-term won't just be the ones with the most GPUs or the lowest per-hour price — they'll be the ones that deliver a managed AI cloud experience: fast onboarding, strong per-tenant isolation, and an operational layer that works transparently in the background.

That requires getting the orchestration stack right. Not as an afterthought. As the product.

Frequently Asked Questions

What is a neocloud?

A neocloud is a new type of cloud infrastructure provider specifically designed for AI and high-performance computing (HPC) workloads. They offer access to large clusters of GPUs at a significantly lower cost than traditional hyperscalers like AWS or Azure.

Unlike general-purpose clouds, neoclouds focus on providing high-density, specialized hardware like NVIDIA H100s connected with high-speed interconnects. This purpose-built infrastructure allows them to optimize for performance and cost, making them a popular choice for training large language models (LLMs) and running inference at scale.

Why are neoclouds cheaper than hyperscalers?

Neoclouds are cheaper because their infrastructure and operational models are purpose-built for AI workloads, avoiding the overhead of supporting a vast portfolio of general-purpose services. This specialization allows them to offer raw GPU power at a fraction of the cost of hyperscalers like AWS or Azure.

The article highlights that an NVIDIA DGX H100 instance can be up to 66% cheaper on a neocloud. This cost difference comes from specialized infrastructure, leaner operations, and a focus on a specific market segment, allowing them to pass significant savings on to customers.

What is the biggest challenge when using a neocloud?

The biggest challenge with most neoclouds is the incomplete orchestration layer, which creates significant operational friction for engineering teams. While they provide excellent GPU hardware, the software stack for managing tenants, isolation, and day-to-day operations is often immature.

This "orchestration gap" leads to several common problems: slow and manual onboarding for new users, weak security between workloads (often just using Kubernetes namespaces), and a heavy DIY burden on platform teams to manage the underlying infrastructure.

How does weak tenant isolation on neoclouds create security risks?

Most neoclouds use namespace-level isolation in Kubernetes, which is insufficient for secure tenant isolation. This creates a shared blast radius, where a security breach or resource-hogging workload in one tenant's namespace can impact others on the same physical server.

Namespace isolation separates resources logically but does not provide strong kernel-level or network-level separation. For organizations running sensitive data or offering a platform to multiple customers, this shared environment poses a significant security risk, as it doesn't protect against container breakout attacks.

How does vCluster solve the main challenges of neoclouds?

vCluster solves the orchestration gap in neoclouds by virtualizing the Kubernetes control plane itself. This provides strong, lightweight tenant isolation and enables instant, self-service provisioning of new environments without the overhead of spinning up full physical clusters.

By giving each tenant their own tenant cluster, vCluster directly addresses the key friction points. It replaces slow manual onboarding with automation, upgrades weak namespace isolation to full control plane isolation, and reduces the DIY overhead on customers by providing a managed, scalable orchestration layer on top of the bare metal GPUs.

Who should use a neocloud for AI workloads?

Teams and companies building AI products, training large models, or running inference at scale are ideal candidates for neoclouds. They are especially well-suited for organizations that find the cost of GPUs on traditional hyperscalers to be a significant barrier.

If your primary need is access to large-scale, high-performance GPU clusters and you have the technical expertise to manage a Kubernetes-based environment, a neocloud can offer dramatic cost savings. However, it's crucial to evaluate the provider's orchestration layer to avoid hidden operational costs that can offset the hardware savings.

If you're building a neocloud or an internal AI factory and you're tired of the DIY overhead, see how vCluster closes the gap →